International Journal of Mechanical Engineering and Robotics Research
IJMERR is a scholarly peer-reviewed international scientific journal published bimonthly, focusing on theories, systems, methods, algorithms and applications in mechanical engineering and robotics.
Dynamics-Driven Policy Transfer to Heavy Wheel-Legged Robots via Imitation and Optimization
Policy transfer is an efficient approach for developing specific robots. Its effectiveness depends on high-quality imitation datasets and a stable learning process. However, substantial differences in geometry and dynamics between source and target robots pose challenges. Purely kinematics-driven mapping methods and manual parameter tuning often fail to maintain kinematic-dynamic consistency. In this study, we transfer control policies from the quadruped robot Unitree Go1 to our self-developed heavy wheel-legged robot Tiangou. We propose a Consistency-Aware Retargeting (CAR) method. This extends conventional inverse kinematics by adding dynamic consistency constraints. Using motion data from Go1’s MPC controller, CAR generates a reference dataset for Tiangou. We then integrate Bayesian Optimization (BO) into the imitation learning framework. This enables autonomous tuning of policy model structures and optimization hyperparameters. Experiments show that CAR reduces foot-end position errors, mitigates joint angular velocity fluctuations, and decreases foot-end slippage. Moreover, Bayesian optimization improves sample efficiency and training stability. These contributions establish a practical foundation for policy transfer across heterogeneous robotic platforms.
Topic: Recent Advances in Mobile Robotics Navigation
Copyright (c) 2026 International Journal of Mechanical Engineering and Robotics Research
Modified Connective Assembly Model for Error Propagation Analysis of High-Precision Assembly of Mechanical Components
High-precision mechanical assemblies require accurate error propagation models to precisely predict error accumulation in the final assembly and to ensure optimal performance and reliability. Conventional linear models exhibit limited accuracy in predicting geometric errors. To address these limitations, this study proposes a modified connective assembly model based on second-order nonlinear error propagation using homogeneous transformation matrices. The proposed model is implemented in Python, and its effectiveness is validated against existing linear and fully nonlinear connective assembly models to evaluate its accuracy and computational efficiency. At an angular orientation error of 1°, the linear model exhibits a fixed error of 0.40 mm in the Z-direction, failing to capture the error of 0.10 mm observed in the fully nonlinear model. The developed model reduces this discrepancy to 0.23 mm, achieving a 56.67% improvement in accuracy over the linear model. Similarly, under 1 mm geometric run-out tolerance, the linear model exhibits an error of -1.60 mm in the Z-direction, while the fully nonlinear model predicts -1.70 mm error. The modified model narrows this gap to -1.66 mm and delivers a 60% improvement. Moreover, the execution time comparisons based on Monte Carlo simulation (MCS) show that the proposed model is computationally efficient, achieving a 48.39% reduction in execution time compared to the fully nonlinear model.
Dynamic Modeling and Control of Pneumatic Artificial Muscles-Driven Multi-Fingered Robots for Circular Object Manipulation
Recently, great concerns have been brought up when applying biologically-inspired robots in rehabilitation for individuals with movement disability. This type of robots must have a higher level of safety feature- provided by more flexible construction. Pneumatic Artificial Muscle (PAM) is driven by compressed air and its performance is similar to biological muscles. Then PAMs are great candidates of actuators for rehabilitation robots. This paper investigates a control algorithm for a multi-fingered robot actuated by PAMs in grasping and manipulating a circular-shaped object. By using the Lagrange method in combination with the natural force-length-velocity relationship of the contracting muscle, a dynamic model for the general robot-object system was formulated. Based on this dynamic model, control algorithms for stable grasping and dexterous manipulation of the object by the multi-fingered robot were proposed. The asymptotic convergence of the closed-loop dynamics was analyzed using the Lyapunov principle and the extended LaSalle invariance theorem. Simulation results further validated the effectiveness of the proposed control algorithms.
Topic: Autonomous Robots in Industry 4.0
Development of Flexible Clamping Devices for Precision Workholding of Irregular and Thin-Walled Workpieces
This paper presents the design, simulation, and experimental validation of a flexible clamping fixture specifically developed for machining irregular and thin-walled workpieces components that are traditionally difficult to fixture due to their non-uniform contours and low structural stiffness. The proposed system features modular segmented jaws capable of conforming to a wide range of geometries without damaging the part or requiring custom fixture fabrication. Through detailed CAD design and finite element analysis, the fixture's structural integrity, clamping force distribution, and deformation behavior are rigorously evaluated. Experimental results confirm the system’s adaptability and effectiveness in securely holding asymmetric and delicate parts under real machining conditions. The developed fixture not only reduces setup time but also enhances positioning accuracy, repeatability, and overall productivity, offering a practical and versatile solution for modern precision manufacturing.
irregular workpieces, thin-walled workpieces, flexible clamping, fixture
Copyright (c) 2025 International Journal of Mechanical Engineering and Robotics Research
Optimization Strategies for Electric Propulsion in UAVs: A Review of Technologies, Control Systems, and Environmental Impacts
This paper presents a systematic literature review of recent developments in the optimization of electric propulsion systems for Unmanned Aerial Vehicles (UAVs). The study focuses on technological advancements, control system enhancements, and environmental impacts to provide a holistic perspective on current trends and future research directions. A total of 42 peer-reviewed articles, published between 2014 and 2024, were analyzed using the PRISMA method to ensure transparency and rigor in selection. The findings show a significant shift toward intelligent optimization techniques, especially Particle Swarm Optimization (PSO) and its hybrid variants, which are widely used for tuning Fuzzy PID controllers to enhance system performance and stability. Furthermore, innovations in lightweight composite materials and the integration of Internet of Things (IoT) technologies have demonstrated improved endurance and adaptability of UAV propulsion systems. Despite these advancements, most studies remain at the simulation stage, with limited real-world implementation. Environmental modeling, thermal-aware control, and lifecycle analysis are identified as key areas requiring further investigation. This review highlights the necessity of cross-disciplinary approaches to address the complexity of electric propulsion optimization. Recommendations for future work include the adoption of embedded ML models, experimental validation frameworks, and system-level integration with aerodynamic and mission planning subsystems.
Design, Programming, and Evaluation of an Economic Portable Muscle Activity Measurement Device
Current commercial EMG systems struggle with four main drawbacks: limited mobility, high prices, time-sensitive processing demands, and their function suitability for a wide range of clinical and research environments. This study will solve these issues through the development of an affordable compact EMG system that ensures real-time processing stability. The system design unites surface electrodes with a low-noise amplifier and a microcontroller having performance-enhanced processing ability. The design requirements specify a signal spectrum from 0 to 250 Hz and precise resolution because this combination leads to the correct measurement of muscular activities. The device performed tests through a series of experiments with healthy participants to measure signal accuracy, noise levels, and usability conditions in controlled and semi-controlled testing environments. The results showed a mean signal-to-noise ratio of 28.5 dB with accuracy levels equivalent to commercial systems, which proved that the device provides clean EMG signals across diverse conditions. Results of noise power analysis showed 0.020 µV² in the resting state but decreased to 0.012 µV² when muscles reached their peak activation strength since effective noise reduction measures were implemented. Despite its advantages including cost-effective operation along with portability, the system presents restrictions in usage with clinical patients who need additional validation of its effectiveness. The portable EMG device presents a versatile, low-cost apparatus for rehabilitation services, sports science testing, and healthcare monitoring needs and requires continuing development for practical implementation.
Special Issue: Intelligent Mechatronics Systems: Advances in Autonomous Control and Robotics (Closed)
Expert-Rule-Based Gain Adjustment for PID and Fuzzy Controllers under Step and Continuous Inputs
Robotic manipulators' ability to perform precise and repeated operations makes them indispensable in industrial settings. Even with advances in control strategies, achieving ideal performance over several input profiles remains a challenging problem. Four control systems are evaluated in this work: fuzzy logic control, fuzzy expert system for step input control, fuzzy expert system for smooth function control, and the fuzzy-based expert-rule gain adjustment method. Using smooth input profiles across joints R, T, and Z, the method examines the system's response to step setpoints of 10, 3.75, and 5, as well as larger step setpoints of 30, 11.25, and 15. The performance criteria assessed include overshoot, rise time, settling time, integral absolute error, and root mean square error. Under all testing conditions, the results show that the suggested approach consistently outperforms the others. Its low overshoot and root mean square error for step setpoints ensure accurate and consistent results. Larger step setpoints demonstrate excellent performance with balanced metrics, emphasizing scalability and robustness. Especially in joints T and Z, it shows notable adaptability to smooth input profiles and achieves low integral absolute error and root mean square error. These results highlight how effectively the system combines the advantages of step and smooth input control. This work shows that under different input conditions, the proposed control method guarantees accuracy, stability, and adaptability, offering a reliable and flexible solution for dynamic robotic systems. For practical applications, it marks a significant improvement in robotic manipulator performance.
Assoc. Prof. Dr. Phichitphon Chotikunnan
Characterization and Validation of R410A Condensation Performance in Split Air Conditioning Units: Experimental Investigation and ANSYS Fluent Simulation
This study introduces a novel approach to optimizing the condensation performance of R410A refrigerant in split air conditioning systems, offering actionable insights for industry and addressing critical challenges of energy efficiency and climate adaptation in regions such as the Kurdistan Region of Iraq. By integrating advanced experimental instrumentation with high-fidelity Computational Fluid Dynamics (CFD) modeling, it presents a comprehensive investigation of two-phase flow dynamics across the full length of an 8.914 m copper condenser tube. Forty-one precisely calibrated temperature sensors and pressure transducers provide real-time monitoring of thermal and flow behavior, while CFD simulations employ the Volume of Fluid (VOF) method with geometric reconstruction and level set interface tracking to accurately predict phase distributions and vapor void fraction. Results reveal three distinct condensation zones: a desuperheating region (0-1.8 m) where superheated vapor rapidly cools, a condensation zone (1.8-6 m) characterized by near-saturation temperatures and a sharp decline in vapor fraction, and a subcooling zone (6-8.9 m) dominated by liquid-phase sensible heat removal. The correlation between experimental and CFD results demonstrates robust model validation, with an overall prediction error of 20-22%. The study highlights the critical role of stratified two-phase flow in maximizing heat transfer efficiency and confirms that the condenser design achieves complete condensation and subcooling, ensuring only liquid refrigerant exits the system. These findings are directly relevant to manufacturers seeking to improve the performance and reliability of split air conditioning units under demanding climatic conditions, both locally and in similar environments worldwide.
Modeling of the Anti-Slip System Processes in the Wheel-Rail Contact of a New Generation Electric Locomotive
This article is devoted to the creation of a simulation model of anti-slip system processes in the "wheel-rail" contact. For this purpose, the necessary elements and model blocks were created using the specialized software environment Dymola: 1) the Sub-Wagon model of a single wagon using Modelica Mechanics Translational; 2) the traction control and speed regulation system model for the new generation KZ8A locomotive, which is compatible with other Modelica blocks and can be used instead of the ideal wheel without slip; 3) a wheel-rail contact model designed for the analysis of wheelset slip during traction and sliding during braking; 4) a computer model of the anti-slip system. Another feature of this model is the consideration of the geographical characteristics of the real Agadyr-Darya section when simulating the movement of rolling stock, which consists of a single section of the new generation KZ8A electric locomotive and 35 freight wagons. An algorithm was developed using Harel's state machine formalism, implemented in Modelica StateMachine, and experimental adhesion characteristics were obtained for various friction conditions of the rail. The developed model allows for the synthesis of an adaptive speed regulator that takes into account local wheel-rail adhesion characteristics as well as track gradients. The results of this work are aimed at improving the efficiency of rolling stock, reducing fuel/energy consumption, and minimizing wheel and rail wear, which together will result in significant economic benefits.
Reinforcement Learning-Based Adaptive Vibration Control for Smart Structures with Fuzzy Uncertainty Quantification
Vibration suppression in mechanical systems (e.g., aerospace, automotive, and robotics) demands adaptive control strategies to handle nonlinear dynamics and environmental uncertainties. Traditional PID and LQR controllers often fail under varying operational conditions. This paper proposes a hybrid framework integrating Deep Deterministic Policy Gradient (DDPG) reinforcement learning with fuzzy logic to optimize vibration damping in real time. The fuzzy system quantifies uncertainty in sensor data, while the RL agent dynamically adjusts control policies. Experimental validation on a cantilever beam with piezoelectric actuators demonstrates a 34% improvement in settling time over baseline methods under stochastic disturbances.
Adaptive control, vibration, RL_Fuzzy controller, DDPG, PID controller.
Trajectory Tracking for Autonomous Vehicles Using NMPC Method and Semantic Lane Inference
This paper introduces an integrated control system for autonomous ground vehicles, combining Nonlinear Model Predictive Control (NMPC) for lateral trajectory tracking with a fuzzy PID controller for longitudinal speed regulation. A vision-based lane detection algorithm generates the reference trajectory, which the NMPC follows, while the fuzzy PID controller adjusts throttle commands to maintain desired speeds. Simulation tests at 5.0 m/s and 10.0 m/s show the vehicle reaching target speeds within 29-to-50-time steps and achieving stable tracking with RMSE values of 7.60 m and 7.31 m. The fuzzy PID ensures smooth throttle transitions, minimising control chatter and enhancing ride quality. The system demonstrates robustness across varying speeds and is suitable for real-time use. Future efforts will incorporate obstacle avoidance and improve adaptability to dynamic road conditions.
Towards Affordable and Lightweight Humanoid Robots: A Task-Specific Design Approach
The world has entered into the Fifth Industrial Revolution, and the core concept of this revolution is the harmonization of humans and machines, emphasizing a human-centric approach to technological advancements. Humanoid robots are designed to interact with humans or work with humans in human-centric environments. However, full-size humanoid robots are still so expensive that will not be suitable for application-specific activities like classroom teaching purposes. The complex mechanical design and expensive hardware are responsible for this cost. Researchers are also facing challenges to reduce humanoid development costs. This paper proposed a cost-effective design for a humanoid robot that is capable of talking, moving, shaking hands, and communicating with humans. With 31 DOF, Dbot is capable of providing human-like motion. The main material of the mechanical structure is aluminium, which makes it more lightweight and stronger. Instead of using expensive hardware, Dbot utilizes a low-cost hardware system that is suitable for task-specific applications. This paper only discusses the design process for mechanical and hardware systems. The proposed design has been evaluated through experiments to understand the performance of both the mechanical and hardware design. After developing based on this design, Dbot was tested at Daffodil International School and College to assist in classroom teaching, and found that with this design, Dbot can successfully interact with students. This lightweight, cost-effective design represents a significant advancement in humanoid robot applications, with the potential to make humanoid robots more accessible and practical for a variety of task-specific uses.
Assessment of Dynamic Loads on a Railway Track from Open Wagons in a Worn Condition with an Increase in Axial Load
The purpose of the presented article is to evaluate the dynamic loads affecting the railway track during the operation of gondola cars in a worn-out technical condition under conditions of increased axial load. The analysis of the features of the interaction of a gondola car with the upper bed of the railway track, as well as the effect of operational wear of components and elements of trolleys on the nature of the transmitted loads, is carried out. An experimental study and simulation of the dynamics of a gondola car has been performed, taking into account the technical condition indicators, loaded and empty condition of gondolas. The results obtained during the study make it possible to identify critical parameters affecting traffic safety and wear of the railway track, as well as formulate recommendations on acceptable indicators of the coefficient of stability margin and the condition of operation of worn-out rolling stock in the study of horizontal and vertical loads.
Topic: Fracture Mechanics and Fatigue Analysis
A Hybrid Posture Stabilization Approach for the Docking Problem of Autonomous Mobile Robots
Autonomous docking of mobile robots presents significant control challenges due to nonholonomic constraints and physical limitations. This paper addresses these challenges with a dual-controller hybrid framework that achieves precise posture stabilization while respecting system constraints. The first component is an analytically derived time-invariant controller based on a polar coordinate transformation of the robot's kinematic model, enabling bidirectional motion that enhances maneuverability. The second component employs a model predictive control (MPC) approach implemented as a pointwise min-norm (PMN) controller that explicitly handles actuator constraints while tracking references generated by the first controller. This constrained optimization is formulated as a quadratic programming problem that maintains the stabilization properties of the reference signals while satisfying velocity and acceleration limits. Extensive validation through simulations and experiments demonstrates performance improvements over conventional methods, with particular emphasis on practical docking scenarios requiring specific pose constraints. The close alignment between theoretical predictions and experimental results confirms the framework's robustness and applicability to real-world docking operations.
Fault Diagnosis in Vehicle Air Conditioning Systems via Comparison of k-Nearest Neighbors and Random Forest Classification Models
This study compares the performance of k-Nearest Neighbors (kNN) and Random Forest (RF) models for fault diagnosis in vehicle air conditioning systems. A dataset of 745 validation samples and 568 test samples was used, covering seven fault conditions, including normal operation and varying levels of condenser fouling and refrigerant leakage. The models were evaluated using accuracy, precision, recall, and F1 score to measure their fault classification effectiveness. Both models achieved a validation accuracy of 91.68%, with a slight drop to 89.26% during testing, indicating similar generalization abilities. Precision remained consistent across both models, while recall and F1 scores varied. The kNN model showed higher recall, indicating better detection of true positive fault cases, while the RF model exhibited more balanced performance with stable precision and F1 scores across datasets. ROC curve analysis revealed that RF outperformed kNN, especially in distinguishing multiple fault classes with overlapping features. However, both models struggled with intermediate levels of condenser fouling, as highlighted by confusion matrix analysis, suggesting a need for improved feature differentiation. Overall, the RF model demonstrated greater stability and robustness, making it more suitable for diagnosing vehicle air conditioning faults where consistency is critical.
Design and Performance Assessment of a Mini Underwater Robot
This study focuses on the design and experimental evaluation of a mini underwater robot specifically for detecting leaks in tanks or reactors. This mini-robot are designed to overcome the challenges of inspecting and maintaining reactors, especially in areas that are difficult to access. A primary concern for effective navigation is motion stability; unstable movement can compromise data accuracy and leak detection precision. The robot operates as a remotely operated vehicle (ROV), controlled from above the water via a wired connection that supplies both power and telemetry. It is equipped with four 60 mm diameter propellers, each featuring three CW/CCW blades, and a front-mounted camera. Constructed from Polylactic Acid (PLA), the robot's dimensions are 390 x 85 x 85 mm, and it is powered by a LiPo battery. The selection of PLA offers a balance of lightweight construction and sufficient structural integrity for underwater maneuvers. Testing occurred in a site measuring 5700 x 2080 x 1100 mm, providing a controlled environment to assess the robot's performance. Experiments involved guiding the robot along various paths to assess its reliability. The results demonstrate effective control with minimal error rates on certain paths, highlighting the importance of stable movement and the potential of these mini underwater robots for intricate underwater operations. This demonstrates the potential for deploying such robots in complex industrial environments where precision and stability are paramount.
Robust Control of a 2D Rehabilitation Robot Using Admittance and RBF Neural Network
In the dynamic model of the 2D rehabilitation robot, factors such as modeling errors, uncertain external disturbances, and friction are present. To achieve compliant force interaction control, a hybrid control strategy integrating admittance control, adaptive RBF neural network compensation, and sliding mode control is proposed in this paper.First, the desired velocity is generated based on the interaction force at the robot handle through admittance control, allowing compliance to be maintained during the interaction process, thereby enhancing the comfort and safety of rehabilitation training.Second, the unknown nonlinear dynamics of the system are approximated in real time using an RBF neural network, and an adaptive learning rate mechanism is designed to accelerate the network's convergence and improve the compensation accuracy.Finally, sliding mode control is employed to compensate for neural network approximation errors and external disturbances, thereby enhancing the robustness and stability of the closed-loop system.Through simulation experiments, the proposed method has been demonstrated to outperform the pure admittance control strategy in terms of trajectory tracking accuracy, response speed, and disturbance rejection capability, verifying its effectiveness and superiority.
Admittance Control; RBF Neural Network; Sliding Mode Control; Adaptive Control; Rehabilitation Robot
Crack Interactions of Non-Coplanar Internal and External Surface Cracks of Hollow Thin Cylinders
The interaction of noncoplanar internal and external surface cracks in hollow thin cylinders plays a critical role in structural integrity assessments, particularly under cyclic and multi-axial loading conditions. This study investigates the behavior of noncoplanar parallel cracks under tension, bending, torsion, and mixed-mode loading, focusing on the distribution of normalized stress intensity factors (SIFs) and the interaction effects along the crack front. Results indicate that external cracks exhibit asymmetric SIF distributions with both amplification and shielding effects, while internal cracks primarily experience shielding. Crack depth (a/t ratio) and separation distance (s/L) significantly influence the interaction magnitude, with deeper cracks showing stronger amplification effects. Under torsional loading, shielding dominates, and interaction effects diminish with increased crack separation. Additionally, the inclination angle affects crack interaction behavior, with external cracks demonstrating greater amplification, making them more critical for structural failure. The findings provide valuable insights into crack growth behavior and fracture-mechanics in thin-walled cylinders, aiding in the development of improved damage tolerance design and life extension strategies for critical structural components.
crack interaction, non-coplanar surface cracks, hollow thin cylinder, stress intensity factor, ANSYS finite element analysis, fracture mechanics, crack assessment
Energy-Saving Research on Battery-Powered Load-Haul-Dump Machine Based on Dual Motor Drive and Intelligent Gear-shifting Control
With the increasing environmental requirements in the mining industry, battery-powered underground Load-Haul-Dump (LHD) machines have become mainstream mining equipment due to their zero emissions and low noise. However, the limited battery capacity poses a critical challenge in improving energy utilization efficiency and extending the working time per charge. This study proposes an energy-saving strategy based on dual motor drive and intelligent gear-shifting control to optimize energy efficiency. Firstly, a model of the LHD’s drive system and energy consumption is established to analyze energy usage characteristics under different working conditions. Then, an adaptive genetic algorithm is employed to optimize transmission system parameters. Furthermore, an intelligent energy-saving gear-shifting strategy based on a Backpropagation Neural Network (BPNN) is proposed to achieve precise gear control and reduce energy consumption. Simulation results indicate that the proposed strategy reduces energy consumption per working cycle by 12.7%, significantly enhancing the economic efficiency and operational performance of the LHD. This research provides a theoretical basis and engineering reference for energy-saving control of underground LHDs.
Cutting Force and Cutting Temperature Prediction in Drilling Using Finite Element Analysis
Drilling is a crucial metal cutting operation extensively employed in the mechanical engineering sector. Predicting the temperature and cutting forces (forces and torques) occurring during drilling on any material and affecting the drill bit will play a positive role in estimating the life of the drill bit and thus achieving better hole quality and accuracy. In this study, drilling force and temperature were studied during Aluminum drilling using finite element method as an alternative to experimental. This topic aims to determine the cutting force and temperature in the cutting zone by analyzing the input parameters of cutting speed and feed rate during the simulation of a high-speed steel drill bit on AL 7075-T6 material. This paper introduces a methodology for developing two finite element models: the finite element method (FEM) and smoothed particle hydrodynamics (SPH) for drilling simulation. The simulations are performed using the LSDYNA code based on available real experiments. The accuracy of the model makes it a satisfactory approach for drilling experiments. The two presented models can verify the drilling process and predict the heat and cutting forces occurring during drilling operation.
Solution of the Positioning Problem for a Planar Parallel Manipulator Based on a Third-Class Mechanism
This paper presents a kinematic analysis of a three-link manipulator system, focusing on the calculation and visualization of joint angles (q1, q2, q3) as functions of the end-effector coordinates (xS, yS) and the orientation angle (α). The goal is to determine the values of the generalized coordinates (q1, q2, q3) that allow the manipulator's working body (WB) to reach a given spatial point S in its workspace. The system's configuration and inverse kinematics problem are addressed through geometric and trigonometric constraints, with the joint angles determined by solving relevant equations involving the manipulator’s link lengths and the positioning of the end-effector. We introduce a redundancy elimination technique by minimizing the positioning error, ensuring the accuracy of the manipulator's trajectory. The results are visualized using 3D plots, which depict the relationship between the joint angles and the coordinates of the manipulator, aiding in the understanding of the system's behavior under different configurations. The proposed method demonstrates efficient handling of kinematic redundancy and offers insights into optimizing the manipulator's positioning accuracy. The findings contribute to improving the control and motion planning of robotic systems in precision tasks.
Advanced Diagnosis of Bearing Defects: A Multi-Domain Approach
It is possible to identify faulty rolling-element bearings before they fail. Regularly measuring the overall vibration level at the bearing housing is the simplest method for detecting such issues. However, routinely evaluating the vibration spectrum provides the earliest potential warning. Rolling element bearings, which combine the functions of both rollers and balls, are commonly found in many applications. Rotary shafts are used in a wide range of devices, including gas turbines, power generators, water pumps, motorcycles, conveyors, and various industries. This work introduces a new approach for detecting, analyzing, and diagnosing rolling element bearing defects. The study compares these defects across three important parameters: the time domain, frequency domain, and spectrum plot. To enhance the effectiveness of spectrum analysis, the study considers the spectrum generated from faults in the outer race and inner race, along with normal conditions.
Diagnosis of Rolling Element Bearing Defects; Time Domain; Frequency Domain; and Spectrum Plot Approaches
The Procedure for Establishing the Permissible Speeds of Semi-Wagons
The article presents an analysis of the permissible speeds of rolling stock during straight-line movement and when passing curves of different radii. The dynamics, structural strength of metal constructions, track impact, parameters of unbalanced acceleration in curves, and stability against wheel derailment are considered. The methodology is based on calculations of dynamic characteristics and modeling the interaction between rolling stock and track infrastructure. The results can be applied in the design of railway transport, modernization of rolling stock, and the development of regulatory documentation to ensure traffic safety
Safe Reinforcement Learning Using Sequential Constraints for Collision Avoidance
A high level of safety has been considered as a key issue in collision avoidance algorithms because collisions are fatal for robots. In this paper, we present a novel safe reinforcement learning (SafeRL) approach for robots to learn a collision avoidance technique using sequential constraints. First, we classify the risk of collision into the following three danger levels: safe, risky, and critical levels. In the safe level, the output control of RL is directly applied to the robot’s navigation because of its low risk. In the risky level, a control barrier function (CBF) is used to ensure the safety of the robot while achieving its control objectives. While the CBF is primarily used to prevent the robot from entering unsafe areas, but it does not provide the ability to recover once it has entered a dangerous area. Therefore, a recovery function (RF) should be used to avoid obstacles at the critical level when the obstacle is too close to the robot. The RF helps the robot to regain stability or return to a safe state after a disturbance or constraint violation. Finally, we propose a control recovery and barrier function (CRBF) based on CBF and RF, which calculates constraints at each level of danger and performs different controls depending on the risk of collision. We conducted in both simulated and real environments to compare the results with existing collision avoidance algorithms. Our method achieved a collision avoidance success rate 5.2%-22.5% higher than APF and ORCA algorithms in environments where obstacles are faster than the robot.
Hybrid Modelling, Control and Simulation of Knee Joint Actuated by Antagonistic Pneumatic Artificial Muscles
This paper presents a hybrid modeling and control strategy for a knee joint actuated by antagonistic McKibben pneumatic artificial muscles. The approach integrates a static force model, which captures the relationship between muscle contraction ratio and internal pressure, and a dynamic model that accounts for friction effects. A compressible flow model describes how pressure is established within each muscle during inflation and released during deflation. By unifying these elements with a differential equation for the knee joint’s motion, a comprehensive representation of the system is developed. A Proportional–Integral–Derivative (PID) controller manages the discrepancy between the reference trajectory and the actual knee angle, adjusting the valve orifice area to regulate airflow accordingly. Simulation studies performed in Simulink confirm the validity of the static muscle model against published data, while the dynamic and flow models are benchmarked against relevant experimental results. The system’s closed-loop performance is evaluated using multiple trajectories—including square wave, triangular, sine wave, and a human gait cycle—quantified through root mean square error (RMSE), maximum absolute error (MAE), and normalized RMSE (NRMSE). Findings indicate that this methodology provides a robust basis for future advancements in pneumatic actuation for robotics applications.
McKibben pneumatic artificial muscles, Knee joint, antagonistic actuation.
The Effect of Pitch Angle Amplitudes on a Cyclorotor Performance Using ComputationalFluid Dynamics
Currently, multirotor UAVs commonly use propellers rotating the vertical axis. However, the cyclorotor is a type of multirotor that the propellers rotate around the lateral axis. The advantages of this type are more compact, stiffer, and safer than conventional rotors. This article focuses on studying the aerodynamic behavior of cyclorotors. The objective of this article is to study the effect of pitch angle amplitudes on the lift, propulsive force, torque, and lift-to-drag ratio using 2D Computational fluid dynamics (CFD) simulation. The variables of this study were as follows: the pitch angle amplitudes of 25, 35, 45 degrees and the rotor speeds consisted of 400, 500, 600, 700, and 800 RPM. As a result, (i) The pitch angle amplitude of 45 degrees provided the highest lift of 29.717 N at 800 RPM. (ii) The pitch angle amplitude of 45 degrees produced the maximum propulsive force of 11.026 N at 800 RPM. (iii) The pitch angle amplitude of 25 degrees offered the lowest torque of 0.239 N.m at 400 RPM. (iv) The pitch angle amplitude of 25 degrees promoted the highest lift-to-torque ratio of 12.705 at 800 RPM.
Van and Truck Classifications of 3D LiDAR Perception for Autonomous Vehicle Navigation
Autonomous vehicles, known as self-driving cars, rely heavily on the 3D LiDAR sensor alongside other sensors to navigate autonomously. 3D LiDAR sensor produces sparse point clouds which makes classifying and detecting objects inside them difficult. However, the introduction of deep learning made it possible which led to various types of 3D object detectors for 3D point clouds. Existing 3D object detectors can identify cars, pedestrians, and cyclists. However, on the road with the abundant presence of heavy vehicles like trucks and vans, this paper explores the potential to retrain an existing 3D object detector, PointPillars, using transfer learning, to also detect vans and trucks in the 3D LiDAR. By training it to do so, autonomous vehicles can recognize and respond appropriately to heavy vehicles during navigation. Then by using Average Precision metric, the results showed an average performance for ‘Van’ but a below-average performance for ‘Truck’. Interestingly, there’s an improvement on the Average Precision score for the ‘Car’ and ‘Cyclist’ classes with a drop on the ‘Pedestrian’ class. The modified PointPillars achieved mean Average Precision (mAP) scores of 35.53 and 34.57 for the 'Van' class using Bird's Eye View (BEV) and 3D metrics, respectively, at IoU=0.5 and at ‘Moderate’ difficulty. On the other hand, the mAP scores for the 'Truck' class are 10.09 and 4.46 using BEV and 3D metrics, respectively at IoU=0.5 and at ‘Moderate’ difficulty.
3D Machine Learning, LiDAR, transfer learning, autonomous vehicles (AV)
Integrating Vision-Language Models for Enhanced Robotic Grasping and Interaction Using RGB Image and Prompt
Object detection and grasping is one of the critical challenges in robotic research, particularly when working in complex environments with diverse objects in terms of shape and position. Although methods using RGB images have shown promising results in simpler scenarios, they still face numerous issues in more complex scenes, especially when objects overlap. Furthermore, prior research has primarily focused on object grasping, without focusing on addressing the interaction capabilities between robots and users during the grasping process. Recent advancements in vision-language models have opened up significant potential for the development of human-robot interaction systems based on multimodal data. This paper presents an integrated model combining computer vision and language models to enhance object detection and grasping capabilities in real-world environments. The proposed approach consists of three key steps (1) identifying the locations of objects and generating segmentation masks using a visual-language model; (2) grasp candidates are predicted from the generated masks and bounding boxes via the Grasp Detection Head; and (3) the candidates are optimized and refined using the Grasp Refinement Head. The integration of vision-language models in the proposed approach not only enhances the ability of robot to understand the semantics of language, enabling more accurate grasping decisions, but also strengthens the interaction capabilities of robot with users. Experimental results demonstrate that the proposed model achieves higher grasping accuracy compared to existing methods, particularly in complex scenes with multiple objects. Proposed model achieved 93.12% accuracy on Jacquard dataset. Additionally, the model also demonstrates its ability to understand complex contexts through Interactive Grasp experiments.
Assist-as-Needed Gait Rehabilitation via Actuator Torque Estimation for a Self-Balancing Lower-Body Exoskeleton
Individuals with partial lower-limb impairments can benefit from robotic gait rehabilitation to enhance muscle memory and coordination. Lower-limb exoskeletons can provide assistance tailed to a patient's needs, reducing therapist workload by automating the procedure. However, the level of assistance provided by the robot should be controlled, such that the user receives assist as needed, while remaining engaged in the motion. The main focus of this paper is to introduce a human-robot interaction estimation technique that enables a self-balancing exoskeleton to asses patient-initiated joint torques; using only robot actuator torque estimations. An admittance controller is used to adjust the deviation from the target based on the effort of the user. The proposed method is tested in simulation environment, to control the swing foot of an exoskeleton biped robot. The simulations show promising results, indicating the potential for applying the method in experiment and more complex motions.
Dual-adaptive suspension system for fighter jet air-inlet inspection robots
This paper presents the comparative analysis of the dual adaptability mechanical suspension system for a mobile robot inspector of the air intakes of a Dassault Mirage 2000P fighter jet with independent suspension coil springs and solid axes, which are two of the most widely used suspension systems for wheeled mobile robots for confined space inspection. This mechanism allows the variation of the relative position of each wheel thanks to two lateral joints that link the two pairs of longitudinal wheels to a central joint that controls the camber. The experimental analysis compared the mechanical advantages of adaptability, maneuverability and locomotion efficacy with two of the most used suspension types in mobile wheeled robots for inspecting pipelines, ventilation ducts, tunnels and collapsed buildings due to the partial geometrical similarity of their working environments with the inspection route of air intakes in airplanes. Experimental locomotion tests are carried out on flat and cylindrical surfaces generated from the geometric decomposition of the inspection area of the fighter plane to finally perform them on the air intakes of the aircraft, demonstrating a significant traction and maneuverability advantage over conventional systems on compound surfaces based on concave and convex curves.